在小样本下联合估计有序分布，提升搜索行为分析精度。
Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
- 基于随机序约束构建整数凸二次规划模型。
- 小样本时平均降低2.2%的JS散度（最高6.3%）。
- 适合分布间有先后关系的用户行为分析场景。
我们研究了多个离散单峰分布的同时估计问题，动机来自真实平台上的搜索行为分析。为融入分布间的先后关系先验知识，引入随机序约束，并将估计任务建模为混合整数凸二次优化问题。在合成数据和真实数据上的实验表明，当样本量较小时，所提方法平均降低2.2%的Jensen-Shannon散度（最高达6.3%），而在数据充足时性能与现有方法相当。
原文摘要 · Abstract (English)
We study the problem of estimating multiple discrete unimodal distributions, motivated by search behavior analysis on a real-world platform. To incorporate prior knowledge of precedence relations among distributions, we impose stochastic order constraints and formulate the estimation task as a mixed-integer convex quadratic optimization problem. Experiments on both synthetic and real datasets show that the proposed method reduces the Jensen-Shannon divergence by 2.2% on average (up to 6.3%) when the sample size is small, while performing comparably to existing methods when sufficient data are available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。